Data Operations Consultant

The Hartford•Wayne, PA

About The Position

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. Hartford Funds is seeking a highly skilled and reliable Data Operations Consultant to join our data team. This individual will be responsible for ensuring the accuracy, reliability, and operational excellence of the organization’s data ecosystem. This role oversees daily data operations, manages triage workflows, drives data quality initiatives, partners closely with internal stakeholders and external data providers, and leads long term improvements that reduce operational risk and enhance trust in the firm’s data.

Requirements

Bachelor’s degree in Information Systems, Data Management, Business, Analytics, or related field.
5+ years of experience in data operations, data quality, data governance, or related data management disciplines.
Experience working in a data‑driven or regulated industry (financial services, asset management, insurance, etc.) preferred.
Strong understanding of data lifecycle, data pipelines, ingestion workflows, and data dependencies.
Strong proficiency in SQL (ability to interpret and write queries and validation rules).
Exposure to data quality tools, workflow tools, monitoring dashboards, and metadata management systems.
Experience with issue‑tracking or triage systems (Jira, ServiceNow, etc.).
Understanding of data modeling, lineage, and data governance concepts.
Strong understanding of how data is used by the business to support reporting, analytics, decisions, and client communications.
Proven ability to translate data issues into business impact.
Experience running cross‑functional meetings and managing complex operational workflows.
Skilled in developing process documentation, SOPs, and operational playbooks.
Excellent communication skills, able to simplify technical issues for business stakeholders.
Strong organizational skills with the ability to manage multiple priorities simultaneously.
Demonstrated ability to lead others through influence rather than authority.
Highly analytical, detail‑oriented, and proactive in problem-solving.

Nice To Haves

Experience with Python or data automation workflows.
Knowledge of BI tools (Tableau).
Familiarity with data governance frameworks.

Responsibilities

Lead the daily data triage process, managing issue intake, prioritization, escalation, and resolution.
Oversee end‑to‑end operational workflows for data ingestion, processing, validation, and delivery.
Ensure data pipelines run reliably and exceptions are handled quickly and consistently.
Develop and maintain SLAs for issue resolution and operational processes.
Monitor data quality KPIs including accuracy, completeness, timeliness, and consistency across core datasets.
Define and maintain data validation rules, quality thresholds, and operational controls.
Conduct root cause analysis of recurring issues and partner with engineering and business teams to implement preventative solutions.
Manage data quality framework scorecards and operational reporting.
Identify opportunities to streamline processes, reduce manual effort, and increase automation.
Standardize data operations and quality management practices across teams.
Create documentation, playbooks, and process maps to ensure repeatability and reduce tribal knowledge.
Contribute to data governance standards related to data quality, lineage, metadata, and data definitions.
Serve as a primary point of contact for data consumers, providing updates on data availability, issues, changes, and quality improvements.
Facilitate communication and resolution with external data providers and internal technology partners.
Build relationships across business lines to understand data usage, critical data elements, and downstream impacts.
Provide monthly communications and status updates on data operations and quality initiatives.
Enhance team engagement, collaboration, and communication across the team and department.
Coach analysts in both technical and business skills related to data quality and operations.
Foster a culture of accountability, operational excellence, and continuous improvement.